Prior art image segmentation methods have been applied in medical imaging to detect micro-calcifications in mammogram, to classify cancerous tissues in MRI images, to evaluate bone structures from X-ray images, to detect lesions in images of organs, or to segment and to classify tumors shown in ultrasound images.
It has been found that prior art methods present several shortcomings, such as when segmenting bones represented by ultrasound images. Prior art methods also present issues when attempting to create a bone structure model using various types and qualities of bone images. For example, errors may occur from time to time depending on image quality (e.g. detecting a bone contour where in fact the image shows a tone variation representative of different body tissues). In addition, typical techniques are rather complex and consuming in terms of time and processing resources. Such drawbacks can become quite irritating for a surgeon during a surgical procedure for example.
A need therefore exists to address prior art shortcomings, including complexity and rapidity of execution issues.